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wav2tok: Deep Sequence Tokenizer for Audio Retrieval

Codes for Reproducibility

Paper link: https://openreview.net/forum?id=v8Mi8KU6056

Citation (bibtex):

@inproceedings{banerjee2023wav2tok,
 title={wav2tok: Deep Sequence Tokenizer for Audio Retrieval},
 author={Banerjee, Adhiraj and Arora, Vipul},
 booktitle={The Eleventh International Conference on Learning Representations},
 year={2023}
 }

Repository Structure:

wav2tok/Src

make 3 more folders bin, weights, Dataset

wav2tok/Src

   /bin 
   
   /weights
   
   /Dataset

Training wav2tok

Keep dataset in wav2tok/Dataset

Make a list of data splits and save as .bin file wav2tok/bin

audios.bin == [X_train , X_test]

X_train, X_test -> lists of audio paths

                   [audio path 1, audio path 2, ...]

OR

Make a list of data dictionaries and save as .bin file to wav2tok/bin

  audios.bin == [X_train , X_test]

  X_train, X_test -> dictionaries of audio

  Keys -> Classes or labels (song_id1, song_id2)

  Values -> List of audio paths (10 utterances for song_id1)


            {class 1 : list of audio , class 2 : list of audio ...}

Code for Training wav2tok:

We have a dedicated function for training a wav2tok model.

                 wav2tok/Src/train.py

Functions used in wav2tok/Src/train.py:

 wav2tok() from wav2tok/Src/wav2tok.py

Trainer() from wav2tok/Src/training_function_library.py

To train a wav2tok model just run in command prompt,

 python3 train.py --args1 arg_value1 --args2 arg_value2

Arguments to pass:

Details of Args for Trainer(...) function

--debug -> int, 1 for debug mode, 0 for work mode  

--dataset -> str, Dataset filename (dataset: {filename}.bin)

--is_dict -> int (Default: 0 -> False), if Dataset is a dictionary or list 

--sample_subdataset -> int (Default: 0 -> False), sample random subsets of data for training on Large datasets 
                       Works only if --is_dict == 0

--subdata_split -> float (Default: 0.1), How big of a portion are the subdatasets in comparison to the large dataset

--is_triplet -> int (Default:  0 ->  False), if you want to train with Batches of Triplets (anchor, positive, negative)
                
--is_single -> int (Default:  0 ->  False), if you want to train with batches of audio (anchor)
    

##########    Default Training is done with pairs of audio (anchor, positive) ##############


--same_length -> int (Default:  0 ->  False), if you want to time stretch audios in each batch of (anchor) or (anchor. positive), (anchor, positive, negative) to same length  

--apply_augmentation -> int (Default:  0 ->  False), works if is_dict == True, apply augmentations to pairs sampled from dictionary === (anchor, positive), apply augmentation to positive

--batch_size -> int (Default: 4), Training batch size

--EPOCHS -> int (Default: 100), Number of full data passes 

--autosave_epoch -> int (Default: 5), autosave model parameters in {autosave} number of epochs

--patience -> int (Default: 5), stop training if evaluation metric doesn't increase for {patience} number of epochs

--name -> str (Default: 'TrialTok' ), Model parameters save filename 

--epoch_start -> int (Default: 0), To start training at {epoch_start} epoch.

--device -> str (Default: 'cuda'), GPU device name

Details on Args for optimizer, learning rate scheduler, weight saving and loading

  --learning_rate ->   float (Default: 2e-3), Learning rate for Training (we use the ADAM optimizer with default settings for training)
  
  --use_scheduler -> int (Default:  1 -> True), if you want to use a learning rate scheduler (We use a linear learning rate scheduler with warmup)
  
  --train_steps -> int (Default: None, Calculated as EPOCHS* dataset_length //batch_size), number of training steps

  --warmup -> float (Default: 0.08), Percentage of training steps to be used for warm up 

  --load_dir -> str (Default: None), Model name to load
  
  --load_model_epochid -> int (Default: None), Epoch id to load 
  
  --best_model -> int (Default:  1 -> True), if you want to load the best version of model

Details of Args for class wav2tok

--debug -> int (Default: 0), 1 for debug mode, 0 for work mode  

--use_transformer -> int (Default:  0 -> False), if you want to use a transformer network as encoder ,
                                         but you have to set the args in wav2tok/Src/wav2tok.py
                                         in class TransformerEncoder and TransformerSentenceEncoderLayer

                                         We use BiLSTM encoder, you can tweak parameters 
                                                        in wav2tok/Src/wav2tok.py class Emb

--input_dim -> int (Default: 39), input Feature dim (STFT dim or MFCC dim) 

--emb_dim -> int (Default: 256), Embedding dim (encoder output dim)

--num_tokens ->  int (Default: 50), number of tokens to use for tokenization 

--num_layers -> int (Default: 2), number of layers to use for BiLSTM model (no effect if you want to use Transformer) 
      
--device -> str (Default: 'cuda'), GPU device name

--dataset -> str (Default: None),Dataset name for clustering ('audios') / takes the training spilt for clustering

--mfcc -> int (Default:  0 -> False), if you want to use MFCC features

--cluster_split -> float (Default: 1.0), percentage of training data to use for clustering (data is sampled randomly)
           
--iter_clust -> int   (Default: 500), number of training steps before each clustering session

--clip -> int (Default:  0 -> False), works if is_dict = False, if you want to clip the to some duration
 
--clip_duration -> float (Default: 3), clip audio to {clip_duration} seconds

--sr -> int (Default: 16000), sampling rate of audio

--use_cosine -> int  (Default:  0 -> False), use cosine similarity in matching task instead of parameterized similarity score
       
--temp -> float (Default: 0.1), temperature for the logits used in cross-entropy calculation
    
--alpha , --beta -> floats  (Default: 0.01,0.01) positive constants in likelihood loss

Brief on the functions present in class wav2tok:

 forward -> input: seq1 , seq2, training_steps

       output: loss with gradients, logs



cluster -> input: dataset name -> string ('audios')

       output: Performs clustering and 
               sets the token classifier codebook



 get_feats -> input: audio-> wav , mfcc -> Boolean 

         output: MFCCs if MFCC == True else STFT matrix 
                 (you can the parameters for extraction of features 
                  manually inside the code segment )



get_embs -> input: audio -> STFT or MFCC

        output: numpy array of Embeddings


initialize_classifier -> input ->  Codebook of token representations, 
                               shape: (number of tokens, Embedding dim)
                     output -> sets token classifier codebook as input





ctc_loss_cal -> input: logits of shape (Time, classes), token sequence

            output: CTC loss or likelihood loss


gen_prototype -> input: Concatenated sequences of representations {Z, Z'},
                    Concatenated sequences of tokens {T,T'},
                    Unique tokens in concatenated sequence {T,T'}
        
             output: Dictionary of Prototypes {token: prototype or average representation in {Z,Z'}  mapping to token }



matching_loss -> input: Dictionary of Prototypes

             output: contrastive loss 



inter_dict_weights -> calculates distances from codebook representations
                  Helper function to matching_loss

Weight Saving and loading

The Trainer function saves the best weights as well as weights every 5 (default) epochs

Uses load_Weights and save_Weights functions in wav2tok/Src/new_function_library.py

save_weights -> input: model instance, epoch_id, name

            output: save weights in wav2tok/weights/{name}_{epoch}.bin



load_Weights -> input: model instance, epoch_id, name 

            output: load weight to model

Code Examples

Args to pass to wav2tok/Src/train.py for different cases of audio dataset

Case 1:

wav2tok/bin/audio.bin == [X_train, X_test]

X_train, X_test -> dictionaries {class 1 : list of audio , class 2 : list of audio ...}

python3 train.py --dataset audios --sr 16000 --is_dict 1 --cluster_split 1.0  --apply_augmentation 1 \
                 --iter_clust 1000 --input_dim 39 --emb_dim 256 --num_tokens 50 --batch_size 16 --name TrialTok

apply_augmentation = True, if you want to sample another sequence of same class 
                       and apply augmentation to it



                 False , if you want to only sample another sequence of same class

Case 2:

wav2tok/bin/audio.bin == [X_train, X_test]

X_train, X_test -> list of audio paths [audio path 1, audio path 2, ...]

 python3 train.py --dataset audios --sr 16000 --is_dict 0 --cluster_split 1.0 --iter_clust 1000 \
                    --input_dim 39 --emb_dim 256 --num_tokens 50 --batch_size 16 --name TrialTok


apply_augmentation = doesn't matter similar sequence generated via audio augmentations